Anti-correlation and subsector structure in financial systems

With the random matrix theory, we study the spatial structure of the Chinese stock market, American stock market and global market indices. After taking into account the signs of the components in the eigenvectors of the cross-correlation matrix, we detect the subsector structure of the financial systems. The positive and negative subsectors are anti-correlated each other in the corresponding eigenmode. The subsector structure is strong in the Chinese stock market, while somewhat weaker in the American stock market and global market indices. Characteristics of the subsector structures in different markets are revealed.Comment: 6 pages, 2 figures, 4 table

Common Scaling Patterns in Intertrade Times of U. S. Stocks

We analyze the sequence of time intervals between consecutive stock trades of thirty companies representing eight sectors of the U. S. economy over a period of four years. For all companies we find that: (i) the probability density function of intertrade times may be fit by a Weibull distribution; (ii) when appropriately rescaled the probability densities of all companies collapse onto a single curve implying a universal functional form; (iii) the intertrade times exhibit power-law correlated behavior within a trading day and a consistently greater degree of correlation over larger time scales, in agreement with the correlation behavior of the absolute price returns for the corresponding company, and (iv) the magnitude series of intertrade time increments is characterized by long-range power-law correlations suggesting the presence of nonlinear features in the trading dynamics, while the sign series is anti-correlated at small scales. Our results suggest that independent of industry sector, market capitalization and average level of trading activity, the series of intertrade times exhibit possibly universal scaling patterns, which may relate to a common mechanism underlying the trading dynamics of diverse companies. Further, our observation of long-range power-law correlations and a parallel with the crossover in the scaling of absolute price returns for each individual stock, support the hypothesis that the dynamics of transaction times may play a role in the process of price formation.Comment: 8 pages, 5 figures. Presented at The Second Nikkei Econophysics Workshop, Tokyo, 11-14 Nov. 2002. A subset appears in "The Application of Econophysics: Proceedings of the Second Nikkei Econophysics Symposium", editor H. Takayasu (Springer-Verlag, Tokyo, 2003) pp.51-57. Submitted to Phys. Rev. E on 25 June 200

Nonlinear dynamics of giant resonances in atomic nuclei

The dynamics of monopole giant resonances in nuclei is analyzed in the time-dependent relativistic mean-field model. The phase spaces of isoscalar and isovector collective oscillations are reconstructed from the time-series of dynamical variables that characterize the proton and neutron density distributions. The analysis of the resulting recurrence plots and correlation dimensions indicate regular motion for the isoscalar mode, and chaotic dynamics for the isovector oscillations. Information-theoretic functionals identify and quantify the nonlinear dynamics of giant resonances in quantum systems that have spatial as well as temporal structure.Comment: 24 pages, RevTeX, 15 PS figures, submitted Phys. Rev.

Comprehensive Analysis of Market Conditions in the Foreign Exchange Market: Fluctuation Scaling and Variance-Covariance Matrix

We investigate quotation and transaction activities in the foreign exchange market for every week during the period of June 2007 to December 2010. A scaling relationship between the mean values of number of quotations (or number of transactions) for various currency pairs and the corresponding standard deviations holds for a majority of the weeks. However, the scaling breaks in some time intervals, which is related to the emergence of market shocks. There is a monotonous relationship between values of scaling indices and global averages of currency pair cross-correlations when both quantities are observed for various window lengths $\Delta t$.Comment: 13 pages, 10 figure

Quantifying trading behavior in financial markets using Google Trends

Crises in financial markets affect humans worldwide. Detailed market data on trading decisions reflect some of the complex human behavior that has led to these crises. We suggest that massive new data sources resulting from human interaction with the Internet may offer a new perspective on the behavior of market participants in periods of large market movements. By analyzing changes in Google query volumes for search terms related to finance, we find patterns that may be interpreted as “early warning signs” of stock market moves. Our results illustrate the potential that combining extensive behavioral data sets offers for a better understanding of collective human behavior

Portuguese and Brazilian stock market integration : a non-linear and detrended approach

Besides the historical heritage that Portugal and Brazil share, the last two decades have also shown an increase in some economic indicators, such as the percentage of imports/exports and foreign direct investment. In order to take advantage of all the benefits, the countries should increase economic integration, stock market integration being one of the possibilities. In this context, this paper analyses stock market integration between these two countries, using non-linear methodologies: detrended fluctuation analysis, detrended cross-correlation analysis and detrended moving-average cross-correlation analysis. Using the main stock indexes, and splitting the sample in six different periods, the main conclusion is that integration between these two countries increased over time. However, since 2013, the integration pattern has decreased, with the economic crisis both countries suffered being the main factor.info:eu-repo/semantics/publishedVersio

Time-dependent ARMA modeling of genomic sequences

<p>Abstract</p> <p>Background</p> <p>Over the past decade, many investigators have used sophisticated time series tools for the analysis of genomic sequences. Specifically, the correlation of the nucleotide chain has been studied by examining the properties of the power spectrum. The main limitation of the power spectrum is that it is restricted to stationary time series. However, it has been observed over the past decade that genomic sequences exhibit non-stationary statistical behavior. Standard statistical tests have been used to verify that the genomic sequences are indeed not stationary. More recent analysis of genomic data has relied on time-varying power spectral methods to capture the statistical characteristics of genomic sequences. Techniques such as the evolutionary spectrum and evolutionary periodogram have been successful in extracting the time-varying correlation structure. The main difficulty in using time-varying spectral methods is that they are extremely unstable. Large deviations in the correlation structure results from very minor perturbations in the genomic data and experimental procedure. A fundamental new approach is needed in order to provide a stable platform for the non-stationary statistical analysis of genomic sequences.</p> <p>Results</p> <p>In this paper, we propose to model non-stationary genomic sequences by a time-dependent autoregressive moving average (TD-ARMA) process. The model is based on a classical ARMA process whose coefficients are allowed to vary with time. A series expansion of the time-varying coefficients is used to form a generalized Yule-Walker-type system of equations. A recursive least-squares algorithm is subsequently used to estimate the time-dependent coefficients of the model. The non-stationary parameters estimated are used as a basis for statistical inference and biophysical interpretation of genomic data. In particular, we rely on the TD-ARMA model of genomic sequences to investigate the statistical properties and differentiate between coding and non-coding regions in the nucleotide chain. Specifically, we define a quantitative measure of randomness to assess how far a process deviates from white noise. Our simulation results on various gene sequences show that both the coding and non-coding regions are non-random. However, coding sequences are "whiter" than non-coding sequences as attested by a higher index of randomness.</p> <p>Conclusion</p> <p>We demonstrate that the proposed TD-ARMA model can be used to provide a stable time series tool for the analysis of non-stationary genomic sequences. The estimated time-varying coefficients are used to define an index of randomness, in order to assess the statistical correlations in coding and non-coding DNA sequences. It turns out that the statistical differences between coding and non-coding sequences are more subtle than previously thought using stationary analysis tools: Both coding and non-coding sequences exhibit statistical correlations, with the coding regions being "whiter" than the non-coding regions. These results corroborate the evolutionary periodogram analysis of genomic sequences and revoke the stationary analysis' conclusion that coding DNA behaves like random sequences.</p

Measurement of the Branching Fraction of the Decay B+→π+π−ℓ+νℓ\boldsymbol{B^{+}\to\pi^{+}\pi^{-}\ell^{+}\nu_\ell} in Fully Reconstructed Events at Belle

We present an analysis of the exclusive $B^{+}\to\pi^{+}\pi^{-}\ell^{+}\nu_{\ell}$ decay, where $\ell$ represents an electron or a muon, with the assumption of charge-conjugation symmetry and lepton universality. The analysis uses the full $\Upsilon(4S)$ data sample collected by the Belle detector, corresponding to 711 fb$^{-1}$ of integrated luminosity. We select the events by fully reconstructing one $B$ meson in hadronic decay modes, subsequently determining the properties of the other $B$ meson. We extract the signal yields using a binned maximum-likelihood fit to the missing-mass squared distribution in bins of the invariant mass of the two pions or the momentum transfer squared. We measure a total branching fraction of ${{\cal B}(B^{+}\to \pi^{+}\pi^{-}\ell^{+}\nu_{\ell})= [22.7 ^{+1.9}_{-1.6} (\mathrm{stat}) \pm 3.5(\mathrm{syst}) ]\times 10^{-5}}$, where the uncertainties are statistical and systematic, respectively. This result is the first reported measurement of this decay.Comment: 23 pages, 19 figure

Study of Leading Hadrons in Gluon and Quark Fragmentation

The study of quark jets in e+e- reactions at LEP has demonstrated that the hadronisation process is reproduced well by the Lund string model. However, our understanding of gluon fragmentation is less complete. In this study enriched quark and gluon jet samples of different purities are selected in three-jet events from hadronic decays of the Z collected by the DELPHI experiment in the LEP runs during 1994 and 1995. The leading systems of the two kinds of jets are defined by requiring a rapidity gap and their sum of charges is studied. An excess of leading systems with total charge zero is found for gluon jets in all cases, when compared to Monte Carlo Simulations with JETSET (with and without Bose-Einstein correlations included) and ARIADNE. The corresponding leading systems of quark jets do not exhibit such an excess. The influence of the gap size and of the gluon purity on the effect is studied and a concentration of the excess of neutral leading systems at low invariant masses (<~ 2 GeV/c^2) is observed, indicating that gluon jets might have an additional hitherto undetected fragmentation mode via a two-gluon system. This could be an indication of a possible production of gluonic states as predicted by QCD.Comment: 19 pages, 6 figures, Accepted by Phys. Lett.

Determination of the b quark mass at the M_Z scale with the DELPHI detector at LEP

An experimental study of the normalized three-jet rate of b quark events with respect to light quarks events (light= \ell \equiv u,d,s) has been performed using the CAMBRIDGE and DURHAM jet algorithms. The data used were collected by the DELPHI experiment at LEP on the Z peak from 1994 to 2000. The results are found to agree with theoretical predictions treating mass corrections at next-to-leading order. Measurements of the b quark mass have also been performed for both the b pole mass: M_b and the b running mass: m_b(M_Z). Data are found to be better described when using the running mass. The measurement yields: m_b(M_Z) = 2.85 +/- 0.18 (stat) +/- 0.13 (exp) +/- 0.19 (had) +/- 0.12 (theo) GeV/c^2 for the CAMBRIDGE algorithm. This result is the most precise measurement of the b mass derived from a high energy process. When compared to other b mass determinations by experiments at lower energy scales, this value agrees with the prediction of Quantum Chromodynamics for the energy evolution of the running mass. The mass measurement is equivalent to a test of the flavour independence of the strong coupling constant with an accuracy of 7 permil.Comment: 24 pages, 10 figures, Accepted by Eur. Phys. J.